Abstract
We propose a hybrid representation of syntactic structures, combining constituency and dependency information. The headed constituency trees that we use offer the advantages of both those approaches to representing syntactic relations within a sentence, with a focus on consistency between them. Based on this representation, we introduce a new constituency parsing technique capable of handling discontinuous structures. The presented approach is centred around head paths in the constituency tree that we refer to as spines and the attachments between them. Our architecture leverages a dependency parser and a large BERT model and achieves 95.96% F1 score on a dataset where \(\approx \)10% of trees contain discontinuities.
Work supported by POIR.04.02.00-00-D006/20-00 national grant (Digital Research Infrastructure for the Arts and Humanities DARIAH-PL).
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Notes
- 1.
We use examples in Polish, since its system of 7 grammatical cases makes the grammatical relations more easily visible than in English.
- 2.
- 3.
- 4.
See http://git.nlp.ipipan.waw.pl/constituency/spines-attachments for the code and dataset.
- 5.
In other words, the tasks (1) and (2b) can be seen as converting the dependency structure to constituencies. Note, however, that the constituency trees are more detailed, so this process adds information.
- 6.
We use the lower subscript NP \(_i\) to differentiate between two different NP nodes, and not to introduce a separate category NP \(_i\).
- 7.
- 8.
- 9.
- 10.
Since the number of distinct spines is fairly limited, we decided to treat them as atomic labels.
- 11.
For the bracketings metric, each span is counted only one time, e.g. for the tree in Fig. 4, (Dam)
and (Dam)
are treated as the same span (Dam) etc. - 12.
We noticed that when validation data loss was used for early stopping and model selection, the accuracies on validation data still exhibited a growing tendency.
- 13.
Let \(TP_l\), \(FP_l\), \(FN_l\) denote the number of true positives, false positives and false negatives respectively for label l in evaluation data. For a set of labels S, we calculate the aggregate precision P\(_S\) as \(({\sum _{l \in S}TP_l})/({\sum _{l \in S}TP_l + FP_l})\), i.e. the proportion of correctly predicted labels from S to all predicted labels from S. The aggregate recall R\(_S\) is \(({\sum _{l \in S}TP_l})/({\sum _{l \in S}TP_l + FN_l})\), i.e. the proportion of correctly predicted labels from S to all gold labels from S. The aggregate F1\(_S\) is the harmonic mean of P\(_S\) and R\(_S\).
- 14.
and (Dam)
are treated as the same span (Dam) etc.References
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Krasnowska-Kieraś, K., Woliński, M. (2023). Constituency Parsing with Spines and Attachments. In: Mikyška, J., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2023. ICCS 2023. Lecture Notes in Computer Science, vol 14073. Springer, Cham. https://doi.org/10.1007/978-3-031-35995-8_14
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